1. Field of the Invention
The present invention generally relates to methods and systems for defect discovery and inspection sensitivity optimization using automated classification of corresponding electron beam images.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers to promote higher yield in the manufacturing process and thus higher profits. Inspection has always been an important part of fabricating semiconductor devices. However, as the dimensions of semiconductor devices decrease, inspection becomes even more important to the successful manufacture of acceptable semiconductor devices because smaller defects can cause the devices to fail.
Information beyond simple defect detection is often generated during inspection processes. For example, the detected defects are often classified into different groups. In one such example, after finding defects, they may be classified into different groups based on the defect characteristics such as size, magnitude, and location. Defects can also be classified based on the information contained within a patch image, a relatively small subsection of the full image. Sometimes, the context in which a defect was found cannot be determined from a patch image alone, requiring a larger section of the image surrounding the defect.
Defect classification often cannot be performed based on just images or information generated by a wafer inspection tool. In these instances, additional information may be generated using a defect review tool and defect classification is then determined based on the additional information. In some such instances, defects found by an optical defect finding apparatus may be reviewed using a high resolution scanning electron microscope (SEM) review tool. In addition, relatively large numbers of SEM images may be collected. Each of these images may be displayed on a computer screen and a user may use the displayed images to determine if there is a defect present in the image. If a defect is detected by eye, the defect can then be classified by the user into one of several categories depending on its shape, size, location or other attributes.
There are, however, several disadvantages to the presently used defect classification methods performed using images generated by a defect review tool. For example, the manual classification of SEM images is substantially labor intensive and time consuming. In addition, many defects are substantially difficult to observe visually. As such, some subtle defects may be easily overlooked. Accuracy and purity of the classification is also user-dependent.
Accordingly, it would be advantageous to develop methods and systems for classifying defects detected on a wafer that do not have one or more of the disadvantages described above.